Diagnosis of Fault Modes Masked by Control Loops with an Application to Autonomous Hovercraft Systems
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Abstract
This paper introduces a methodology for the design, testing and assessment of incipient failure detection techniques for failing components/systems of critical engineered systems/processes masked or hidden by feedback control loops. It is recognized that the optimum operation of critical assets (aircraft, autonomous systems, industrial processes, etc.) may be compromised by feedback control loops, which mask severe fault modes while compensating for typical disturbances. Detrimental consequences of such occurrences include the inability to detect expeditiously and accurately incipient failures, loss of control, and inefficient operation of assets in the form of fuel overconsumption and adverse environmental impact. A novel control-theoretic framework is presented to address the masking problem. Major elements of the proposed approach are employed in simulation to develop, implement and validate how faults are distinguished from disturbances and how faults are detected and identified with performance guarantees, i.e., prescribed confidence level and given false alarm rate.
The demonstration and validity of the tools/methods employed necessitates, in addition to the theoretical content, a suitable testbed. We have employed and describe briefly in this paper an autonomous hovercraft as the test prototype. We pursue a systems engineering process to design, construct and test the prototype hovercraft instrumented appropriately for purposes of fault injection, monitoring and the presence of control loops. We emphasize a general control-theoretic framework to the masking problem and utilize a simulation environment to derive results and illustrate the efficacy of the methodology.
##plugins.themes.bootstrap3.article.details##
hovercraft, autonomous, FDIR, Fault Masking
Breivik, M., & Fossen, T. I. (2008). Guidance laws for planar motion control. Paper presented at the Decision and Control, 2008. CDC 2008. 47th IEEE Conference on.
Brinker, J. S., & Wise, K. A. (2012). Stability and flying qualities robustness of a dynamic inversion aircraft control law. Journal of Guidance Control and Dynamics, 19(6), 1270-1277. doi: Doi 10.2514/3.21782
Buffington, J. M., Adams, R. J., & Banda, S. S. (1993). Robust, nonlinear, high angle-of-attack control design for a supermaneuverable vehicle. Paper presented at the AIAA Guidance, Navigation and Control Conference, Monterey, CA.
Dinca, L., Aldemir, T., & Rizzoni, G. (1999). A model-based probabilistic approach for fault detection and identification with application to the diagnosis of automotive engines. Automatic Control, IEEE Transactions on, 44(11), 2200-2205.
Duda, R. O., Hart, P. E., & Stork, D. H. (2000). Pattern Classification (2nd ed.), Wiley Interscience.
Ioannou, P. A., & Sun, J. (1996). Robust Adaptive Control. New Jersey, Prentice Hall.
Isermann, R. (1984). Process fault detection based on modeling and estimation methods—A survey. Automatica, 20(4), 387-404.
Jones, H. L. (1973). Failure detection in linear systems. Massachusetts Institute of Technology.
Khalil, H. K. (2002). Nonlinear Systems, 3rd. New Jewsey, Prentice Hall.
Koenig, N., & Howard, A. (2004). Design and use paradigms for gazebo, an open-source multi-robot simulator. Paper presented at the Intelligent Robots and Systems, 2004.(IROS 2004). Proceedings. 2004 IEEE/RSJ International Conference on.
Kohlbrecher, S., von Stryk, O., Meyer, J., & Klingauf, U. (2011). A flexible and scalable slam system with full 3d motion estimation. Paper presented at the Safety, Security, and Rescue Robotics (SSRR), 2011 IEEE International Symposium on.
Massoumnia, M. A., Verghese, G. C., & Willsky, A. S. (1989). Failure detection and identification. Automatic Control, IEEE Transactions on, 34(3), 316-321.
OpenDynamicsEngine. (2013). Open Dynamics Engine. from http://www.ode.org/
Orchard, M. E. . (2007). A Particle Filtering-based Framework for On-line Fault Diagnosis and Failure Prognosis. (Ph.D.), Georgia Institute of Technology, Atlanta, GA.
Orchard, M. E., & Vachtsevanos, G. J. (2007). A particle filtering-based framework for real-time fault diagnosis and failure prognosis in a turbine engine. Paper presented at the Control & Automation, 2007. MED'07. Mediterranean Conference on.
Orchard, M. E., & Vachtsevanos, G. J. (2009). A particle-filtering approach for on-line fault diagnosis and failure prognosis. Transactions of the Institute of Measurement and Control, 31(3-4), 221-246.
Pandaboard. (2013). from http://www.pandaboard.org/
Quigley, M., Gerkey, B., Conley, K., Faust, J., Foote, T., Leibs, J., . . . Ng, A. (2009). ROS: an open-source Robot Operating System. Paper presented at the ICRA workshop on open source software.
Srivastava, A. N. (2012). Greener aviation with virtual sensors: a case study. Data Mining and Knowledge Discovery, 1-29.
Voulgaris, Z., & Sconyers, C. (2010). A Novel Feature Evaluation Methodology for Fault Diagnosis. Paper presented at the Proceedings of the World Congress on Engineering and Computer Science.
Willsky, A. S. (1976). A survey of design methods for failure detection in dynamic systems. Automatica, 12(6), 601-611.
Zhang, B., Sconyers, C., Byington, C., Patrick, R., Orchard, M. E., & Vachtsevanos, G. (2011). A probabilistic fault detection approach: application to bearing fault detection. Industrial Electronics, IEEE Transactions on, 58(5).